Method and device for classifying remote images by integrating space information and spectral information

Abstract

The invention relates to a method and a device for classifying remote images by integrating space information and spectral information. The method includes the steps: performing pixel-wise support vector machine classification and area-based image segmentation for the remote images so as to obtain classified images and segmented images respectively; and combining the classified images with the segmented images, and performing majority voting so as to obtain final classified images. Since classified results of the remote images are obtained based on combination of the area-based image segmented results and the pixel-wise classified results, the method and the device are better in robustness, low in computing cost, easy to perform and capable of avoiding the problem of dimensional proportion selection. Besides, the defect that traditional partitional-clustering-segmentation-based classification of the hyperspectral images by integrating the space information and spectral information is non-robust is overcome.

Description

technical field [0001] The present invention relates to a classification method and device for remote sensing images, more specifically, to a classification method and device for remote sensing images that fuse spatial information and spectral information. Background technique [0002] Hyperspectral images with high spectral resolution have a wide range of applications, such as mineral detection, pollution monitoring, precision agriculture, etc. Among them, classification is a very important process of hyperspectral image processing. The classification of traditional hyperspectral images is mainly based on spectral information, and seldom considers spatial information. [0003] Commonly used classification methods for hyperspectral images that fuse spatial and spectral information include Markov random fields, morphological pointers, and morphological filtering. However, these methods use a fixed window to obtain spatial information, which leads to the selection problem of...

AI Technical Summary

Problems solved by technology

However, these methods use a fixed window to obtain spatial information, which leads to the selection problem of scale

Another classification method is to integrate spatial information into a multi-kernel learning method, which also has the problem of size ratio selection, and the computational cost of this method is very high

Another way to fuse spatial information is to fuse the pixel-by-pixel classification results with the segmentation results obtained by Partitional Clustering, but the results of this method are not robust.

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